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Segmentation of 3D objects using live wire

Segmentation of 3D objects using live wire,Alexandre X. Falcao,Jayaram K. Udupa

Segmentation of 3D objects using live wire   (Citations: 17)
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We have been developing user-steered image segmentation methods for situations which require considerable user assistance in object definition. In such situations, our segmentation methods aim (1) to provide effective control to the user on the segmentation process while it is being executed and (2) to minimize the total user's time required in the process. In the past, we have presented two paradigms, referred to as live wire and live lane, for segmenting 3D/4D object boundaries in a slice-by-slice fashion. In this paper, we introduce a 3D extension of the live wire approach which can further reduce the time spent by the user in the segmentation process. In 2D live wire, given a slice, for two specified points (pixel vertices) on the boundary of the object, the best boundary segment (as a set of oriented pixel edges) is the minimum-cost path between the two points. This segment is found via dynamic programming in real time as the user anchors the first point and moves the cursor to indicate the second point. A complete 2D boundary in this slice is identified as a set of consecutive boundary segments forming a 'closed,' 'connected,' 'oriented' contour. The strategy of the 3D extension is that, first, users specify contours via live- wiring on a few orthogonal slices. If these slices are selected strategically, then we have a sufficient number of points on the 3D boundary of the object to do live-wiring automatically on all axial slices of the 3D scene. Based on several validation studies involving segmentation of the bones of the foot in MR images, we found that the 3D extension of live wire is statistically significantly (p less than 0.0001) more repeatable and 2 - 6 times faster (p less than 0.01) than the 2D live wire method and 3 - 15 times faster than manual tracing.
Conference: Storage and Retrieval for Image and Video Databases , vol. 3034, pp. 228-235, 1997
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    • ...Livewire orintelligent scissors wasfirst introduced by[7,8]...

    Jia Tonget al. An Interactive Segmentation Based on CT Image Series

    • ...Our method extends the work of Falcao et al [11] by using a novel approach for acquiring and ordering the data points...
    • ...Our 3D semi-automatic segmentation technique is based on extensions to the classical 2D live-wire method and an earlier extension by Falcao et al [11]...
    • ...Compared to earlier techniques[11], the presented method offers more freedom in choosing the initial live-wire contours, the user need not scan through many slices to explicitly partition the object beforehand, and finally the data point ordering is no longer restricted to their angle but rather based on a connected graph approach that deals with a wide variety of shapes...

    Ghassan Hamarnehet al. 3D live-wire-based semi-automatic segmentation of medical images

    • ...Both measurements require segmentation of the cartilage from the surrounding tissue using such techniques as manual segmentation [15,22,52], signal intensity– based thresholding [15,52], seed-growing algorithms [53], filtering [61–63], watershed [64] and live wire approaches [54,55,65,66], or model-based segmentation [57,67–69]...

    Philipp Langet al. MR Imaging of Articular Cartilage: Current State and Recent Developmen...

    • ...While traditional methods of region-based [1]‐[3], (e.g., region growing, texture analysis) and edge-based [3]‐[6], (e.g., snakes, dynamic programming) image segmentation algorithms continue to be explored with attempts to improve their performance by adding expert knowledge to their detection criteria, new techniques have been developed (active shape models [7], live-lanes and live-wires [8], [9], intelligent scissors [10], [11], ...

    Marek Brejlet al. Object Localization and Border Detection Criteria Design in Edge-Based...

    • ...For live wire only one approach for segmentation of three-dimensional images has been published so far [3] based on the projection of fewinteractively defined contours onto orthogonal cross-sections through the volumetric image...

    Andrea Schenket al. Efficient Semiautomatic Segmentation of 3D Objects in Medical Images

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